Treg cells in mouse GvHD
Nicholas Strieder
2021-03-01
parallel computing
Loading Data
To load 10x data there a several options with pros an cons.
## [1] "RERUNstate= FALSE"batch info
| SampleID | Samplename | Repl | group | cells_seq |
|---|---|---|---|---|
| 2_C | 2C | R_2 | C | 5253 |
| 2_L | 2L | R_2 | L | 2708 |
| 2_S | 2S | R_2 | S | 3879 |
| 235_d12 | 235d12 | R_235 | d12 | 3066 |
| 3_C | 3C | R_3 | C | 4903 |
| 3_L | 3L | R_3 | L | 2580 |
| 3_S | 3S | R_3 | S | 4650 |
| 4_C | 4C | R_4 | C | 4948 |
| 4_L | 4L | R_4 | L | 1860 |
| 4_S | 4S | R_4 | S | 3610 |
Pre-processing and QC
## orig.ident nCount_RNA nFeature_RNA percent.mt percent.rb
## 2C_AAACCTGAGTCGAGTG-1 2C 7717 2210 0.5572114 20.29286
## 2C_AAACCTGCAGCCAGAA-1 2C 5075 1833 0.3349754 17.87192
## 2C_AAACCTGCAGGGCATA-1 2C 5507 1814 0.8534592 24.62321
## 2C_AAACCTGCATTTCAGG-1 2C 8181 2509 0.7089598 17.69955
## 2C_AAACCTGGTCGCTTCT-1 2C 3193 1216 0.7203257 16.72408
## keepall keepnCRlow keepM keepR pANN_0.25_0.09_432
## 2C_AAACCTGAGTCGAGTG-1 TRUE TRUE TRUE TRUE 0.2785924
## 2C_AAACCTGCAGCCAGAA-1 TRUE TRUE TRUE TRUE 0.3724340
## 2C_AAACCTGCAGGGCATA-1 TRUE TRUE TRUE TRUE 0.1260997
## 2C_AAACCTGCATTTCAGG-1 TRUE TRUE TRUE TRUE 0.3255132
## 2C_AAACCTGGTCGCTTCT-1 TRUE TRUE TRUE TRUE 0.2756598
## DF.classifications_0.25_0.09_432 pANN_0.25_0.09_176
## 2C_AAACCTGAGTCGAGTG-1 Singlet NA
## 2C_AAACCTGCAGCCAGAA-1 Singlet NA
## 2C_AAACCTGCAGGGCATA-1 Singlet NA
## 2C_AAACCTGCATTTCAGG-1 Singlet NA
## 2C_AAACCTGGTCGCTTCT-1 Singlet NA
## DF.classifications_0.25_0.09_176 pANN_0.25_0.09_319
## 2C_AAACCTGAGTCGAGTG-1 <NA> NA
## 2C_AAACCTGCAGCCAGAA-1 <NA> NA
## 2C_AAACCTGCAGGGCATA-1 <NA> NA
## 2C_AAACCTGCATTTCAGG-1 <NA> NA
## 2C_AAACCTGGTCGCTTCT-1 <NA> NA
## DF.classifications_0.25_0.09_319 pANN_0.25_0.09_252
## 2C_AAACCTGAGTCGAGTG-1 <NA> NA
## 2C_AAACCTGCAGCCAGAA-1 <NA> NA
## 2C_AAACCTGCAGGGCATA-1 <NA> NA
## 2C_AAACCTGCATTTCAGG-1 <NA> NA
## 2C_AAACCTGGTCGCTTCT-1 <NA> NA
## DF.classifications_0.25_0.09_252 pANN_0.25_0.09_403
## 2C_AAACCTGAGTCGAGTG-1 <NA> NA
## 2C_AAACCTGCAGCCAGAA-1 <NA> NA
## 2C_AAACCTGCAGGGCATA-1 <NA> NA
## 2C_AAACCTGCATTTCAGG-1 <NA> NA
## 2C_AAACCTGGTCGCTTCT-1 <NA> NA
## DF.classifications_0.25_0.09_403 pANN_0.25_0.09_168
## 2C_AAACCTGAGTCGAGTG-1 <NA> NA
## 2C_AAACCTGCAGCCAGAA-1 <NA> NA
## 2C_AAACCTGCAGGGCATA-1 <NA> NA
## 2C_AAACCTGCATTTCAGG-1 <NA> NA
## 2C_AAACCTGGTCGCTTCT-1 <NA> NA
## DF.classifications_0.25_0.09_168 pANN_0.25_0.09_382
## 2C_AAACCTGAGTCGAGTG-1 <NA> NA
## 2C_AAACCTGCAGCCAGAA-1 <NA> NA
## 2C_AAACCTGCAGGGCATA-1 <NA> NA
## 2C_AAACCTGCATTTCAGG-1 <NA> NA
## 2C_AAACCTGGTCGCTTCT-1 <NA> NA
## DF.classifications_0.25_0.09_382 pANN_0.25_0.09_407
## 2C_AAACCTGAGTCGAGTG-1 <NA> NA
## 2C_AAACCTGCAGCCAGAA-1 <NA> NA
## 2C_AAACCTGCAGGGCATA-1 <NA> NA
## 2C_AAACCTGCATTTCAGG-1 <NA> NA
## 2C_AAACCTGGTCGCTTCT-1 <NA> NA
## DF.classifications_0.25_0.09_407 pANN_0.25_0.09_106
## 2C_AAACCTGAGTCGAGTG-1 <NA> NA
## 2C_AAACCTGCAGCCAGAA-1 <NA> NA
## 2C_AAACCTGCAGGGCATA-1 <NA> NA
## 2C_AAACCTGCATTTCAGG-1 <NA> NA
## 2C_AAACCTGGTCGCTTCT-1 <NA> NA
## DF.classifications_0.25_0.09_106 pANN_0.25_0.09_297
## 2C_AAACCTGAGTCGAGTG-1 <NA> NA
## 2C_AAACCTGCAGCCAGAA-1 <NA> NA
## 2C_AAACCTGCAGGGCATA-1 <NA> NA
## 2C_AAACCTGCATTTCAGG-1 <NA> NA
## 2C_AAACCTGGTCGCTTCT-1 <NA> NA
## DF.classifications_0.25_0.09_297 SampleID Repl group
## 2C_AAACCTGAGTCGAGTG-1 <NA> 2_C R_2 C
## 2C_AAACCTGCAGCCAGAA-1 <NA> 2_C R_2 C
## 2C_AAACCTGCAGGGCATA-1 <NA> 2_C R_2 C
## 2C_AAACCTGCATTTCAGG-1 <NA> 2_C R_2 C
## 2C_AAACCTGGTCGCTTCT-1 <NA> 2_C R_2 C
## [1] "Scatter nCounts vs n Features"
Filtering
Normalizing and var Features
## [1] "Gzma" "Ccl4" "Ifitm1" "Trbv17" "Ccl1" "Penk"
## [7] "Hbb-bs" "Hba-a1" "Ecm1" "Areg" "Cxcl2" "Gcg"
## [13] "Hba-a2" "Cxcl10" "Trbv1" "Ccl3" "Trbv15" "Hbb-bt"
## [19] "Trbv29" "Trav8d-2"Split object in batchs
Intergate function of Seurat leads to lost processes on the cluster, forking problem.
Scaling of the data
Perform integration
Perform integrated analysis
PCA
## PC_ 1
## Positive: Vps37b, Bhlhe40, Ifrd1, Tnfaip3, Hilpda
## Negative: Rps8, Rps20, Rpl28, Rpl10a, Ifi47
## PC_ 2
## Positive: AW112010, Ifi27l2a, Ly6a, S100a11, Cxcr3
## Negative: Rpl13, Rps2, Rpl32, Rps5, Igfbp4
## PC_ 3
## Positive: Birc5, Stmn1, Pclaf, Ccna2, Spc24
## Negative: Rpl13, Rps4x, Rps5, Rps18, Rpl8
## PC_ 4
## Positive: S100a6, Vim, Lgals3, Nkg7, Crip1
## Negative: Izumo1r, Stat1, Cd74, Cd81, Cd83
## PC_ 5
## Positive: Lgals1, Vim, Actg1, S100a10, S100a6
## Negative: Zfp36l2, Jun, Evl, Cd7, Btg2VizDimLoadings(SObj, dims = 1:2, reduction = "pca")
DimPlot(SObj, reduction = "pca",label=TRUE,group.by="orig.ident")
DimPlot(SObj,reduction="pca",split.by="group",group.by="orig.ident",label=TRUE)+
geom_hline(yintercept=0)+geom_vline(xintercept=0)
DimPlot(SObj, reduction = "pca",label=TRUE,group.by="orig.ident",dims=c(2,3))
DimPlot(SObj,reduction="pca",dims=c(2,3),split.by="group",group.by="orig.ident",label=TRUE)
Heatmaps to decide which PCs to include, find batch effects
DimHeatmap(SObj, dims = 1, cells = 500, balanced = TRUE)
DimHeatmap(SObj, dims = 1:17, cells = 500, balanced = TRUE)
## Inspect data for SCT
##Determine Dimensionality of Data set:
Cluster cells
## 2C_AAACCTGAGTCGAGTG-1 2C_AAACCTGCAGCCAGAA-1 2C_AAACCTGCAGGGCATA-1
## 0 0 0
## 2C_AAACCTGCATTTCAGG-1 2C_AAACCTGGTCGCTTCT-1
## 0 0
## Levels: 0 1 2 3 4 5 6 7 8 9 10 11 12UMAP
Vis Treg Genes
Find Diff features
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:-------|-----:|----------:|-----:|-----:|---------:|
## |Neurl3 | 0| 1.0663105| 0.681| 0.250| 0|
## |Il18rap | 0| 0.4520373| 0.281| 0.101| 0|
## |Stat4 | 0| 1.1966809| 0.835| 0.472| 0|
## |Stk17b | 0| 0.6193758| 0.960| 0.849| 0|
## |Coq10b | 0| 0.5059204| 0.502| 0.264| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:------|-----:|----------:|-----:|-----:|---------:|
## |Ptpn18 | 0| 0.4603764| 0.986| 0.979| 0|
## |Neurl3 | 0| -0.8881515| 0.142| 0.389| 0|
## |Ctla4 | 0| -1.6912062| 0.603| 0.843| 0|
## |Cd48 | 0| 0.6016163| 0.843| 0.742| 0|
## |Dusp10 | 0| -0.9277337| 0.058| 0.288| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:------|-----:|----------:|-----:|-----:|---------:|
## |Neurl3 | 0| 0.8736130| 0.592| 0.296| 0|
## |Arid5a | 0| 0.8095186| 0.527| 0.342| 0|
## |Stk17b | 0| 0.8925751| 0.954| 0.857| 0|
## |Coq10b | 0| 0.7786504| 0.520| 0.276| 0|
## |Clk1 | 0| 0.8524342| 0.676| 0.404| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:------|-----:|----------:|-----:|-----:|---------:|
## |Ctla4 | 0| -1.9626862| 0.578| 0.825| 0|
## |Icos | 0| -1.1836658| 0.638| 0.815| 0|
## |Rgs1 | 0| -1.8963865| 0.508| 0.751| 0|
## |Slamf6 | 0| 0.7655931| 0.533| 0.252| 0|
## |Ephx1 | 0| 0.8856907| 0.469| 0.140| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:-------|-----:|----------:|-----:|-----:|---------:|
## |Zap70 | 0| 0.6929217| 0.891| 0.758| 0|
## |Cst7 | 0| 1.0398208| 0.924| 0.700| 0|
## |S100a11 | 0| 0.5707854| 0.990| 0.918| 0|
## |Ctss | 0| 0.7796210| 0.760| 0.497| 0|
## |Vps37b | 0| -2.4479675| 0.164| 0.552| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:-------|-----:|----------:|-----:|-----:|---------:|
## |Ramp1 | 0| 1.0998419| 0.457| 0.164| 0|
## |Vim | 0| 1.0934467| 0.994| 0.879| 0|
## |Cdc25b | 0| 0.9080684| 0.568| 0.204| 0|
## |Rps27 | 0| -0.4276138| 0.997| 0.997| 0|
## |S100a13 | 0| 0.6007740| 0.974| 0.853| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:------|-----:|----------:|-----:|-----:|---------:|
## |Rpl7 | 0| 0.8389506| 0.997| 0.983| 0|
## |Zap70 | 0| -1.1414245| 0.610| 0.783| 0|
## |Stat1 | 0| -1.4139616| 0.540| 0.780| 0|
## |Dnah7a | 0| 0.7226934| 0.280| 0.024| 0|
## |Stk17b | 0| -1.1913404| 0.799| 0.880| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:------|-----:|----------:|-----:|-----:|---------:|
## |Stat1 | 0| 1.557797| 0.988| 0.756| 0|
## |Ifi214 | 0| 1.376515| 0.699| 0.198| 0|
## |Ifi209 | 0| 1.800837| 0.956| 0.493| 0|
## |Ifi208 | 0| 1.865837| 0.866| 0.301| 0|
## |Ifih1 | 0| 1.319685| 0.604| 0.152| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:-------|-----:|----------:|-----:|-----:|---------:|
## |Ptma | 0| 1.3117789| 1.000| 0.996| 0|
## |Cenpf | 0| 1.2002792| 0.414| 0.013| 0|
## |Spc25 | 0| 0.5792946| 0.284| 0.015| 0|
## |Selenoh | 0| 1.6368082| 0.896| 0.327| 0|
## |Ccdc34 | 0| 0.6898874| 0.421| 0.059| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:-------|-----:|----------:|-----:|-----:|---------:|
## |Slc40a1 | 0| 2.344474| 0.519| 0.003| 0|
## |Fcer1g | 0| 3.387036| 0.751| 0.025| 0|
## |Gatm | 0| 1.489229| 0.388| 0.004| 0|
## |Slpi | 0| 2.057951| 0.373| 0.008| 0|
## |Vcam1 | 0| 1.997361| 0.435| 0.002| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:------|-----:|----------:|-----:|-----:|---------:|
## |Cxcl10 | 0| 3.725377| 0.624| 0.066| 0|
## |Rsad2 | 0| 2.257266| 0.668| 0.056| 0|
## |Ifit3 | 0| 2.828317| 0.809| 0.104| 0|
## |Ifit3b | 0| 1.671574| 0.470| 0.041| 0|
## |Ifit1 | 0| 2.945491| 0.881| 0.185| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:------|-----:|----------:|-----:|-----:|---------:|
## |Knl1 | 0| 0.7875364| 0.373| 0.017| 0|
## |Rad51 | 0| 0.9049908| 0.546| 0.021| 0|
## |Nusap1 | 0| 0.9009919| 0.394| 0.021| 0|
## |Tpx2 | 0| 1.0250170| 0.470| 0.030| 0|
## |Cks1b | 0| 1.2148313| 0.679| 0.065| 0|
##
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:-----|-----:|----------:|-----:|-----:|---------:|
## |Lmo2 | 0| 2.176814| 0.598| 0.003| 0|
## |Gatm | 0| 1.455458| 0.392| 0.007| 0|
## |Car1 | 0| 3.773360| 0.716| 0.009| 0|
## |Gstm5 | 0| 1.700562| 0.480| 0.008| 0|
## |Rhd | 0| 2.638066| 0.402| 0.001| 0|## Calculating cluster 0## Calculating cluster 1## Calculating cluster 2## Calculating cluster 3## Calculating cluster 4## Calculating cluster 5## Calculating cluster 6## Calculating cluster 7## Calculating cluster 8## Calculating cluster 9## Calculating cluster 10## Calculating cluster 11## Calculating cluster 12## [1] "gene list ordered by avg_log2FC"
## Selecting by gene## [1] "gene list ranked by seurat"
TCR visualisation
## [1] "TCRs included: 55\nTCRs leftout: 28"
## [1] "TCRs included: 57\nTCRs leftout: 39"
## [1] "TCRs included: 60\nTCRs leftout: 30"
Session Info
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 10 (buster)
##
## Matrix products: default
## BLAS/LAPACK: /misc/software/package/RBioC/libmkl/R-4.0.3_buster_intel-mkl-64bit-2020.1-102_build/compilers_and_libraries_2020.1.217/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dplyr_1.0.3 patchwork_1.1.1 ggplot2_3.3.3 cowplot_1.1.1
## [5] future_1.21.0 knitr_1.30 SeuratObject_4.0.0 Seurat_4.0.0
## [9] rmarkdown_2.6
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-149 matrixStats_0.57.0 RcppAnnoy_0.0.18
## [4] RColorBrewer_1.1-2 httr_1.4.2 sctransform_0.3.2
## [7] tools_4.0.3 R6_2.5.0 irlba_2.3.3
## [10] rpart_4.1-15 KernSmooth_2.23-17 uwot_0.1.10
## [13] mgcv_1.8-33 DBI_1.1.1 lazyeval_0.2.2
## [16] colorspace_2.0-0 withr_2.4.1 tidyselect_1.1.0
## [19] gridExtra_2.3 compiler_4.0.3 plotly_4.9.3
## [22] labeling_0.4.2 scales_1.1.1 lmtest_0.9-38
## [25] spatstat.data_1.7-0 ggridges_0.5.3 pbapply_1.4-3
## [28] goftest_1.2-2 spatstat_1.64-1 stringr_1.4.0
## [31] digest_0.6.27 spatstat.utils_2.0-0 pkgconfig_2.0.3
## [34] htmltools_0.5.1.1 parallelly_1.23.0 limma_3.46.0
## [37] highr_0.8 fastmap_1.1.0 htmlwidgets_1.5.3
## [40] rlang_0.4.10 shiny_1.6.0 farver_2.0.3
## [43] generics_0.1.0 zoo_1.8-8 jsonlite_1.7.2
## [46] ica_1.0-2 magrittr_2.0.1 Matrix_1.2-18
## [49] Rcpp_1.0.6 munsell_0.5.0 abind_1.4-5
## [52] reticulate_1.18 lifecycle_0.2.0 stringi_1.5.3
## [55] yaml_2.2.1 MASS_7.3-53 Rtsne_0.15
## [58] plyr_1.8.6 grid_4.0.3 parallel_4.0.3
## [61] listenv_0.8.0 promises_1.1.1 ggrepel_0.9.1
## [64] crayon_1.3.4 deldir_0.2-9 miniUI_0.1.1.1
## [67] lattice_0.20-41 splines_4.0.3 tensor_1.5
## [70] pillar_1.4.7 igraph_1.2.6 future.apply_1.7.0
## [73] reshape2_1.4.4 codetools_0.2-16 leiden_0.3.7
## [76] glue_1.4.2 evaluate_0.14 data.table_1.13.6
## [79] vctrs_0.3.6 png_0.1-7 httpuv_1.5.5
## [82] polyclip_1.10-0 gtable_0.3.0 RANN_2.6.1
## [85] purrr_0.3.4 tidyr_1.1.2 scattermore_0.7
## [88] assertthat_0.2.1 xfun_0.20 mime_0.9
## [91] xtable_1.8-4 later_1.1.0.1 survival_3.2-7
## [94] viridisLite_0.3.0 tibble_3.0.5 cluster_2.1.0
## [97] globals_0.14.0 fitdistrplus_1.1-3 ellipsis_0.3.1
## [100] ROCR_1.0-11